Attention Embedded Residual Bottleneck CNN Architecture for Breast Cancer Diagnosis in Ultrasound Images

Mamuna Fatima; Muhammad Attique Khan; Saima Shaheen; Seifedine Kadry; Omar Alqahtani; M. Turki-Hadj Alouane

doi:10.9781/ijimai.2025.03.005

Authors

Mamuna Fatima HITEC University
Muhammad Attique Khan Prince Mohammad bin Fahd University
Saima Shaheen HITEC University
Seifedine Kadry Lebanese American University
Omar Alqahtani King Khalid University
M. Turki-Hadj Alouane King Khalid University

DOI:

https://doi.org/10.9781/ijimai.2025.03.005

Keywords:

Breast Cancer, Deep Learning, Feature Extraction, Feature Fusion, Feature Optimization

Supporting Agencies

The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through small group research under grant number RGP1/71/45.

Abstract

Breast cancer (BrC) stands as the predominant cancer among women, resulting in a substantial global mortality toll each year. Early detection plays a pivotal role in diminishing mortality rates. Manual diagnosis of BrC is time-intensive, intricate, and prone to errors, emphasizing the necessity for an automated system for timely detection. Various imaging methods have been investigated, underscoring the crucial need for accurate detection to prevent unwarranted treatments and biopsies. Recent years have witnessed substantial exploration and enhancement in the application of DL for efficiently processing medical images. This study aiming to create an effective and resilient DL framework for BrC detection and classification. The steps are contrast enhancement and augmentation, a hybrid CNN network ‘BrC-DeepRBNet’ is introduced that is built from scratch and incorporates several design elements including residual blocks, bottleneck architecture, and a self-attention mechanism. This framework is employed to construct two networks, one comprising of 107 layers and the other with 149 layers. Moreover, the network capitalizes on the benefits offered by batch normalization (BN) and group normalization (GN), utilizes ReLU and leaky ReLU as activation functions, and integrates Max pooling layer into its architecture in a series of residual-bottleneck blocks. Further, for feature fusion horizontal approach is used and optimization is done using generalized normal distribution optimization (GNDO). The selected features are further classified using neural network classifiers. The introduced framework achieved the highest classification accuracy at 97.05% with publicly available BUS dataset. A detailed ablation study is presented that demonstrates the superior performance of the presented approach, surpassing various pre-trained models (i.e. AlexNet, InceptionV3, ResNet50, and ResNet101) and existing BrC detection and classification techniques.

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